Model Predictive Control (MPC) is the most widely used advanced process control technology applied in process industries. There are more than 10,000 worldwide applications currently in service. A MPC controller relies on a process model to predict the process behavior (the controlled variables, CV) and makes changes to the manipulated variables (MV) so that the MPC controller can keep the process running inside a prescribed constraint set. When inside the constraint set, a MPC controller also makes changes relative to the MVs, so that the process behavior is optimized (as steady-state targets) based on a given economic objective function. The MPC controller can be tuned to configure preferred optimizing of the process behavior when formulating steady-state targets by the given economic objective function.
Tuning of the MPC controller to achieve a preferred optimized behavior for an industrial process based on plant operation goals is not a trivial task. Rather, the tuning requires adjusting the cost factors for each MV in the context of the single, given economic objective function, by means of a challenging and time consuming trial and error technique, until the correct balance of cost factors are found to achieve the preferred optimization. Due to the dependencies and interactions among the process variables, the tuning of one MV may have affects that must be balanced across multiple MVs using this trial and error technique. Further, when using the single objective function with a large number of MVs, performing the trade-offs for optimization makes the process behavior sensitive to process model and operating condition changes, resulting in the cost factors needing to be re-tuned often to maintain the preferred optimized behavior. As such, there is a need in the process industry for a method to configure preferred optimization behavior for an industrial process, based on plant operation goals, that does not require challenging and time consuming trial and error tuning of the MPC controller.